{"id":"W4390904977","doi":"10.1162/imag_a_00081","title":"Methods for decoding cortical gradients of functional connectivity","year":2024,"lang":"en","type":"article","venue":"Imaging Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Institute of Mental Health; National Institutes of Health","keywords":"Decoding methods; Computer science; Leverage (statistics); Functional connectivity; Artificial intelligence; Segmentation; Pattern recognition (psychology); Psychology; Neuroscience; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04734316,0.002438598,0.003226167,0.008813749,0.001060632,0.004956972,0.003591305,0.002138451,0.005397078],"category_scores_gemma":[0.1427635,0.001367819,0.008558929,0.007804076,0.001800375,0.003039376,0.002682314,0.003704911,0.001900036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001861671,"about_ca_system_score_gemma":0.004495165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006039605,"about_ca_topic_score_gemma":0.00892156,"domain_scores_codex":[0.9724725,0.02127476,0.001970557,0.002470958,0.001594459,0.0002167822],"domain_scores_gemma":[0.9364843,0.05172957,0.002378613,0.005774094,0.003405931,0.000227405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006930564,0.0001442842,0.01157412,0.007875849,0.01469302,0.0003473943,0.001465772,0.1039691,0.007014938,0.09355199,0.01571514,0.7429553],"study_design_scores_gemma":[0.0003563153,0.0002114067,0.008885196,0.001360701,0.003345547,0.0003690054,0.0004094673,0.5107399,0.006129117,0.4399594,0.02793791,0.0002959668],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001324549,0.00101419,0.9956027,0.0002711087,0.00004433492,0.00021252,0.0005855612,0.0006947182,0.0002502749],"genre_scores_gemma":[0.03298781,0.0007627458,0.9627063,0.0001670212,0.00005004247,0.001440651,0.001158091,0.0004303986,0.0002969651],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04734316,"threshold_uncertainty_score":0.2503775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1008229962498013,"score_gpt":0.3919328946914249,"score_spread":0.2911098984416236,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}